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Research on User Churn Prediction of Music Platform Based on Integrated Learning

Published: 24 October 2024 Publication History

Abstract

This paper combines the background of the era of big data, utilizes the massive user data of well-known digital music platforms, and integrates complex network and integrated learning methods to make user churn prediction. Firstly, data preprocessing is carried out to solve the problem of category imbalance in the dataset. Second, a complex network is constructed based on users' historical behavior logs, and the network features are extracted by mode-link and count-link, which are combined with users' independent features as the total feature inputs, and finally, the features are input into the XGBoost, LightGBM, and Stacking integrated models for prediction. On the member subscription dataset selected in this paper, compared with using the above three models alone, the improved models based on the complex network have all improved in prediction accuracy, providing an effective reference for future user churn prediction in the digital music domain.

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    CAIBDA '24: Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms
    June 2024
    1206 pages
    ISBN:9798400710247
    DOI:10.1145/3690407
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 October 2024

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    Author Tags

    1. complex networks
    2. integrated learning
    3. unbalanced datasets
    4. user churn prediction

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